Alzheimer?s Disease and Alzheimer?s related dementias (ADRD) are now regarded as a public health problem of pressing importance. While the race for disease-altering treatments continues, another strand of work has focused on identifying social and psychological precursors of these conditions. Such biopsychosocial risks often can be traced back to phases of life that predate symptom onset by years or decades. Therefore, a robust social and life course epidemiology of ADRD requires study designs that feature a) broad and deep psychosocial characterization of b) a large, population-relevant cohort c) during early phases of life, with d) medically-documented outcome data. Parent project R01AG053155 features a) through c), specifically in 90,000 members of the Project Talent cohort assessed in 1960 and again in 1970-74. The current supplement expands its scope to all members of Project Talent baseline (roughly 340,000) from 1960, and focuses on two scientific aims. The first seeks to estimate the relative risk of ADRD by the early 70s arising from adolescent personality traits, as documented in Medicare data linked to the cohort. One key feature of this aim is to determine if aspects of personality in adolescence are associated with ADRD incidence in later life independently of adolescent IQ, which is a known predictor. The second key aspect of this aim is to use the size and population representativeness of the sample to derive reasonably precise population-relevant effect size estimates of personality relative risks, and compare these effect sizes to benchmark risk estimates of adolescent IQ and socioeconomic status, which are considered to have policy and public health significance. The second aim also leverages the size and scope of the sample to identify personality traits which may moderate the ADRD risk of low adolescent IQ, in more complex and realistic patterns than can be studied in smaller or less representative data sets. This is accomplished via machine learning methods focused on identifying non-linear interactions via intensive cross-validation, another scientific question that takes full advantage of the size and scope of this unique cohort.